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Beginning a Responsible Data Science Project | part of Responsible Data Science | Wiley Data and Cybersecurity books | IEEE Xplore

Beginning a Responsible Data Science Project

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Chapter Abstract:

This chapter explains the justification, compilation, and preparation stages of the Responsible Data Science framework. As part of the Responsible Data Science framework,...Show More

Chapter Abstract:

This chapter explains the justification, compilation, and preparation stages of the Responsible Data Science framework. As part of the Responsible Data Science framework, data science teams should consider model performance across multiple metrics, as well as broader social, legal, and institutional factors that are relevant to their specific modeling context. Links to the original source of each dataset can be found in the hyperlinks section of each of the project's datasheets. All data scientists have experienced the pain of attempting to replicate another person's work on their own machine. Impact statements for data science became popular, or, more accurately, infamous, on account of the prestigious Neural Information Processing Systems Conference requiring the inclusion of a “Broader Impact Statement” for submissions to the 2020 conference. Data dictionaries are almost ubiquitous across data science projects. The chapter deals with the Communities and Crime dataset.
Page(s): 137 - 172
Copyright Year: 2021
Edition: 1
ISBN Information: